Climate-driven flood hazard assessment in data-scarce mountainous basins using a GIS-based machine learning and hydrodynamic modelling under CMIP6 SSP scenarios

作者：Shahbaz Khan, Afed Ullah Khan, Abdullah Alodah, Ahmad Azeem, Muhammad Waqas, Faten Nahas, Nazih Y. Rebouh, Youssef M. Youssef · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-31390-7 · 被引用次数：6 · 研究领域：Flood Risk Assessment and Management、Hydrology and Watershed Management Studies、Cryospheric studies and observations

Floods pose increasing risks in mountainous regions such as the Swat River Basin, where climatic variability, glacial influences, and data limitations hinder conventional flood risk assessment. This study introduces a hybrid framework that integrates explainable SHapley Additive exPlanations (SHAP)-based XGBoost for Global Climate Model (GCM) ranking, Random Forest (RF) ensemble modeling, and coupled hydrologic–hydraulic simulations (HEC-HMS–HEC-RAS) for multi-scenario flood hazard mapping. The approach provides an interpretable, data-driven, and physically based method for assessing climate-induced flood hazards in data-scarce basins. Daily precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) data from eleven CMIP6 (Coupled Model Intercomparison Project Phase 6) Global Climate Models (GCMs) were bias-corrected using the linear scaling approach. These GCMs were ranked using XGBoost regression with SHAP interpretation, achieving high predictive accuracy (R 2 : 0.934/0.926 for precipitation, 0.953/0.949 for Tmax, and 0.947/0.943 for Tmin). A Multi-Model Ensemble built with RF regression further improved performance (R 2 : 0.74/0.71 for precipitation; 0.97/0.963 for Tmax; 0.965/0.958 for Tmin). These datasets were used to drive the HEC-HMS model, calibrated (1993–2013) and validated (2014–2019) with satisfactory results (NSE: 0.612/0.603; PBIAS: + 3.96%/− 6.75%). Flood frequency analysis using distribution fitting and the VIKOR method identified Log-Logistic...